We present a hybrid method for computing volatility forecasts that can be used to implement
a risk-controlled strategy for a multi-asset portfolio consisting of both US and international …

This paper studies the forecasting power of uncertainty emanating from the commodities
market, energy market, economic policy, and geopolitical threats to the CBOE Volatility Index …

Data stream mining (DSM) represents a promising process to forecast financial time series
exchange rate. Financial historical data generate several types of cyclical patterns that …

Volatility forecasts play a central role among equity risk measures. Besides traditional
statistical models, modern forecasting techniques based on machine learning can be …

This study investigates the Topological Tail Dependence (TTD) theory's applicability to
individual stock volatility and high dimensions. Utilizing a comprehensive dataset from the …

We forecast the realized covariance matrix of asset returns in the US equity market by
exploiting the predictive information of graphs in volatility and correlation. Specifically, we …

We review recent articles that focus on the main issues identified in high-frequency financial
data analysis. The issues to be addressed include nonstationarity, low signal-to-noise ratios …

The paper investigates the issue of volatility of stock index returns on the Warsaw Stock
Exchange (WIG20 index returns volatility). The purpose of this review is to compare how …

In contemporary financial quantitative analysis systems, accuracy usually means lower risks
and higher profits, and latency usually implies more uncertainty. A more accurate implied …

We present a novel nonparametric methodology for modeling and forecasting multivariate
realized volatilities using customized graph neural networks to incorporate spillover effects …